Abstract

Makromantar türleri, ekosistemlerdeki kritik rolleri ve geniş endüstriyel uygulamaları nedeniyle dikkat çekmektedir. Geleneksel tür teşhis yöntemleri uzmanlık gerektiren ve zaman alıcı süreçlerdir; bu nedenle yapay zekâ (YZ), özellikle derin öğrenme (DÖ) teknikleri, bu süreçleri hızlandırmak ve doğruluğu artırmak amacıyla kullanılmaktadır. Bu makale, beş farklı makromantar türünü YZ, özelde DÖ teknikleri kullanarak otomatik olarak sınıflandırmayı amaçlamaktadır. Çalışma kapsamında Amanita muscaria, A. phalloides, Lepista nuda, Macrolepiota procera ve Craterellus cornucopioides türleri ele alınmış; bu türlerin sınıflandırılmasında DenseNet121, InceptionV3, MobileNetV2, Xception, VGG16 ve ResNet101 gibi çeşitli derin öğrenme modelleri kullanılmıştır. Veri kümesi, 5 sınıfta toplam 683 görüntüden oluşmaktadır. Veriler dengeli bir şekilde toplanmış ve modellerin etkinliği doğruluk, kesinlik (precision), duyarlılık (recall) ve F1-skoru gibi metrikler üzerinden değerlendirilmiştir. Ayrıca modellerin hangi bölgelere odaklandığını analiz etmek amacıyla Grad-CAM görselleştirmeleri kullanılmıştır. En iyi performansı gösteren model %93 doğruluk (%7 hata) elde etmiş, %70 doğruluk (%30 hata) sağlayan basit bir Evrimsel Katmanlı Sinir Ağı temel modelini belirgin biçimde geride bırakmıştır; genel olarak, tüm transfer öğrenimi modelleri %90 ve üzeri doğruluklara ulaşmıştır. Özellikle DenseNet121 ve Xception modelleri, makromantar türlerine ait ilgili bölgeleri doğru şekilde tespit ederek en yüksek başarıyı sağlamıştır. Bu çalışma, biyolojik tür teşhisinde YZ, özellikle DÖ tekniklerinin etkin bir şekilde kullanılabileceğini ortaya koymakta ve veri setlerinin genişletilmesinin bu tekniklerin başarısını daha da artırabileceğini göstermektedir. Bu çalışmanın yeniliği, transfer öğrenmeyi Grad-CAM açıklanabilirliği ile birleştirerek makrofungusların tanımlanmasına yönelik yorumlanabilir ve biyolojik açıdan anlamlı bir çerçeve sunmasıdır.

Advanced deep learning approaches for the automated classification of macrofungal species in biodiversity monitoring

Abstract

Macrofungal species attract significant attention due to their critical roles in ecosystems and widespread industrial applications. Traditional species identification methods are expertise-intensive and time-consuming processes. Artificial intelligence (AI) techniques, especially, deep learning (DL), have been employed to accelerate these processes and improve result accuracy. This article aimed to classify five macrofungi using AI, specifically DL. The study focuses on classifying Amanita muscaria, A. phalloides, Lepista nuda, Macrolepiota procera, and Craterellus cornucopioides, utilizing various DL models, including DenseNet121, InceptionV3, MobileNetV2, Xception, VGG16, and ResNet101. The dataset comprised 683 images across five classes. The data were collected in a balanced manner, and the model’s effectiveness was evaluated based on accuracy, precision, recall, and F1-score metrics. Additionally, Grad-CAM visualizations were utilized to analyze the regions of focus. The best-performing model achieved 93% accuracy (7% error), outperforming a simple Convolutional Neural Network baseline with 70% accuracy (30% error). Overall, all transfer-learning models achieved accuracies of ≥ 90%. In particular, the DenseNet121 and Xception models achieved the maximum success by correctly identifying relevant regions of these species. The study demonstrates that AI, particularly DL-based techniques, can be effectively applied in species identification. Expanding datasets could further enhance their performance. The novelty of this study is the use of a combination of transfer-learning and Grad-CAM explainability to provide an interpretable and biologically meaningful framework for macrofungi identification.

Keywords

• Deep Learning
• Macrofungi Classification
• Artificial Intelligence
• Grad-CAM Visualization
• Biodiversity Monitoring

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